Though far from mainstream, machine learning continues to gain momentum in financial services even as experts warn of the consequences of artificial intelligence technologies.

Steve Wilcockson, MathWorks

"...we need to think about machine learning and AI very carefully, work together closely on it, understand the perils and pitfalls, opportunities and ultimately come up with some best practices."

Massive IT disruption is providing the financial industry with
new avenues for business and managing risk, according to a
survey by MathWorks.

Underpinning that disruption is a blend of 'big data'
technologies intersecting with modelling and analytics, both
simple and sophisticated.

Steve Wilcockson, industry manager for Financial Services at
MathWorks, said that when the survey was first conducted in 2012,
the term big data had the feel of hype around it. Now, its
importance in shaping the industry's future seems to be taken for
granted.

"There has been a lot of work done in terms of data aggregation
at the mechanistic level - data exploration, consistency,
interpolation - to make sure we are working with even data," he
said. "There are also developments around some of the areas of
text, or unstructured data."

Moreover, firms are paying attention to the implementation of new
data environments, such as the shift from SQL to NoSQL databases.
There's still a long way to go however.

"Many of the larger institutions have got a big data project on
the go. Some of them have defined what that project looks like,
others are looking for projects," he said. Some 31% of survey
respondents reported that their institution has implemented a
project to react to a specific big data challenge.

Trade execution is one such project being pursued by asset
managers, Wilcockson noted, as the industry starts to learn the
benefits of understanding and modelling the nuances of execution
in the shift to on-exchange dealing. Considering the complexity
of market structure these days, a tier one asset manager wants to
know that a large order for single stock is filled the way
intended.

With a little help from robot friends

The survey shows that some 12% of respondents said they applied
machine learning technologies. However, there is clearly interest
from across the industry to take a closer look - 40% of
respondents said they want to understand more about the different
machine learning methods.

Wilcockson said that when he talks to people from the banking
sector, he sees interest in developing fraud monitoring systems,
or improving trading strategies albeit tempered with caution. Two
major hang-ups have to do with overfitting and blackbox
modelling.

"One of the issues with machine learning is there are lots of
different models, with lots of different nuances which can have
multiple variability when applied to different data sets," he
added.

A "middle way" to automation, said Wilcockson, is emerging as
fund managers use it to validate human trading decisions, where
the automated is working in sync with the human.

The survey comes at a time when artificial intelligence is making
headlines. Renowned physicist Stephen Hawking and CEO of Tesla
Motors Elon Musk have both sounded warnings about unfettered
advances in artificial intelligence. Musk went so far as to write
that it could be "potentially more dangerous than nukes".

The concerns have resulted in an
open letter from the Future of Life Institute, which calls
for AI experts to make a pact to develop artificial intelligence
technologies responsibly. Aside from Musk and Hawking, signees
include academics from top schools as well as business leaders
from Microsoft, IBM and Google.

If a dystopian future of man-machine warfare seems a bit
Hollywood blockbuster, it probably is. But the open letter, which
highlights characteristics of responsible computer science, does
resonate with MathWorks' survey results, Wilcockson said.

"What you are seeing in the survey is kind of the practical
insight into what the open letter recommends, which is that we
need to think about machine learning and AI very carefully, work
together closely on it, understand the perils and pitfalls,
opportunities and ultimately come up with some best practices,"
Wilcockson said.

Still, it is early days, and only the beginning of a cycle that
is also a second wave of interest in AI applications to financial
markets. Neural networks for example were a hot topic in markets
not too long ago, but ultimately fell out of favour.

But this time is different, and the difference is big data.

A year from now, Wilcockson expects the financial sector,
particularly the institutional side, to be testing some of these
capabilities. "They will have got their hands dirty," he said,
adding that beyond 2016, a more systematic roll-out could be in
the cards.

He emphasises that there are many angles to the big data space -
looking at how models are calibrated, the spread of machine
learning, more sophisticated statistical methods, or
investigating data series.

"We will see a trend towards optimisation - how we navigate our
parameter space to hone in onto the best (data) points. There is
a big element of the quant world, of the computer science world,
that is focusing in on this area alongside the AI and I think the
two areas along with the data side are all intersected," said
Wilcockson.

The survey of 78 professionals was conducted at the MATLAB
Computational Finance Conference in London in June 2014.
Respondents included buy and sell side, as well as consultants
and central bank representatives.